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     "start_time": "2023-04-03T06:18:25.076384Z"
    }
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ef22587",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T06:21:50.478186Z",
     "start_time": "2023-04-03T06:21:50.083823Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0d50c0d",
   "metadata": {
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     "end_time": "2023-04-03T06:22:00.124600Z",
     "start_time": "2023-04-03T06:22:00.092707Z"
    }
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>store_sales(in millions)</th>\n",
       "      <th>unit_sales(in millions)</th>\n",
       "      <th>total_children</th>\n",
       "      <th>num_children_at_home</th>\n",
       "      <th>avg_cars_at home(approx).1</th>\n",
       "      <th>gross_weight</th>\n",
       "      <th>recyclable_package</th>\n",
       "      <th>low_fat</th>\n",
       "      <th>units_per_case</th>\n",
       "      <th>store_sqft</th>\n",
       "      <th>coffee_bar</th>\n",
       "      <th>video_store</th>\n",
       "      <th>salad_bar</th>\n",
       "      <th>prepared_food</th>\n",
       "      <th>florist</th>\n",
       "      <th>cost</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>8.61</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.80</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
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       "      <td>66.78</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>5.0</td>\n",
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      "text/plain": [
       "   id  store_sales(in millions)  unit_sales(in millions)  total_children  \\\n",
       "0   0                      8.61                      3.0             2.0   \n",
       "1   1                      5.00                      2.0             4.0   \n",
       "2   2                     14.08                      4.0             0.0   \n",
       "3   3                      4.02                      3.0             5.0   \n",
       "4   4                      2.13                      3.0             5.0   \n",
       "\n",
       "   num_children_at_home  avg_cars_at home(approx).1  gross_weight  \\\n",
       "0                   2.0                         2.0         10.30   \n",
       "1                   0.0                         3.0          6.66   \n",
       "2                   0.0                         3.0         21.30   \n",
       "3                   0.0                         0.0         14.80   \n",
       "4                   0.0                         3.0         17.00   \n",
       "\n",
       "   recyclable_package  low_fat  units_per_case  store_sqft  coffee_bar  \\\n",
       "0                 1.0      0.0            32.0     36509.0         0.0   \n",
       "1                 1.0      0.0             1.0     28206.0         1.0   \n",
       "2                 1.0      0.0            26.0     21215.0         1.0   \n",
       "3                 0.0      1.0            36.0     21215.0         1.0   \n",
       "4                 1.0      1.0            20.0     27694.0         1.0   \n",
       "\n",
       "   video_store  salad_bar  prepared_food  florist    cost  \n",
       "0          0.0        0.0            0.0      0.0   62.09  \n",
       "1          0.0        0.0            0.0      0.0  121.80  \n",
       "2          0.0        0.0            0.0      0.0   83.51  \n",
       "3          0.0        0.0            0.0      0.0   66.78  \n",
       "4          1.0        1.0            1.0      1.0  111.51  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "139f471a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T06:22:11.545698Z",
     "start_time": "2023-04-03T06:22:11.504650Z"
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 360336 entries, 0 to 360335\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   id                          360336 non-null  int64  \n",
      " 1   store_sales(in millions)    360336 non-null  float64\n",
      " 2   unit_sales(in millions)     360336 non-null  float64\n",
      " 3   total_children              360336 non-null  float64\n",
      " 4   num_children_at_home        360336 non-null  float64\n",
      " 5   avg_cars_at home(approx).1  360336 non-null  float64\n",
      " 6   gross_weight                360336 non-null  float64\n",
      " 7   recyclable_package          360336 non-null  float64\n",
      " 8   low_fat                     360336 non-null  float64\n",
      " 9   units_per_case              360336 non-null  float64\n",
      " 10  store_sqft                  360336 non-null  float64\n",
      " 11  coffee_bar                  360336 non-null  float64\n",
      " 12  video_store                 360336 non-null  float64\n",
      " 13  salad_bar                   360336 non-null  float64\n",
      " 14  prepared_food               360336 non-null  float64\n",
      " 15  florist                     360336 non-null  float64\n",
      " 16  cost                        360336 non-null  float64\n",
      "dtypes: float64(16), int64(1)\n",
      "memory usage: 46.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abdafc8d",
   "metadata": {},
   "outputs": [],
   "source": []
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